Derive glucose isSpotEstimate from reading density

Hard-coding isSpotEstimate: true mislabels a dense Nightscout CGM stream
(~288 readings/day) as a spot estimate and undersells the data in the
clinical panel and coach. Compute readings-per-day over the covered span
and treat the series as continuous below the spot caveat once it clears a
~hourly bar (24 readings/day); sparse spot fingersticks keep the caveat.
The panel only renders the spot-reading line when isSpotEstimate, and the
OpenAPI field relaxes from a literal-true to a boolean.

docs/api/openapi.yaml

@@ -14126,7 +14126,6 @@ components:
             - type: "null"
         isSpotEstimate:
           type: boolean
-          const: true
       required:
         - stillLearning
         - stillLearningReason
@@ -14141,8 +14140,9 @@ components:
         - advanced
         - isSpotEstimate
       additionalProperties: false
-      description: Server-authoritative glucose clinical panel over the trailing 30-day window. Every figure is a SPOT-READING
-        ESTIMATE (a % of readings), not a CGM time-in-range AGP — `isSpotEstimate` is always true and `stillLearning`
+      description: Server-authoritative glucose clinical panel over the trailing 30-day window. Figures from sparse spot data
+        are a SPOT-READING ESTIMATE (a % of readings), not a CGM time-in-range AGP; `isSpotEstimate` is derived from
+        reading density (true below ~hourly, false for a continuous CGM stream such as Nightscout) and `stillLearning`
         gates assertion when the window has too few readings or too short a span. `distribution` carries the Battelino
         2019 TIR/TBR/TAR fractions (level-2 nested in level-1) plus minutes-of-a-day equivalents; `gmi` (Bergenstal
         2018) + `estimatedA1c` (Nathan 2008 ADAG) derive from the mean; `variability` is SD + CV% with the Monnier 2017